What can we learn from benefit transfer errors? Evidence from 20 years of research on convergent validity

Sapna Kaul, Kevin J. Boyle, Nicolai V. Kuminoff, Christopher F. Parmeter, Jaren C. Pope

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

We develop a nonparametric approach to meta-analysis and use it to identify modeling decisions that affect benefit transfer errors. The meta-data describe the results from 31 empirical studies testing the convergent validity of benefit transfers. They evaluated numerous methodological procedures, collectively reporting 1071 transfer errors. Our meta-regressions identify several important findings, including: (1) the median absolute error is 39%; (2) function transfers outperform value transfers; (3) transfers describing environmental quantity generate lower transfer errors than transfers describing quality changes; (4) geographic site similarity is important for value transfers; (5) contingent valuation generates lower transfer errors than other valuation methods; and (6) combining data from multiple studies tends to reduce transfer errors.

Original languageEnglish (US)
Pages (from-to)90-104
Number of pages15
JournalJournal of Environmental Economics and Management
Volume66
Issue number1
DOIs
StatePublished - Jul 1 2013

Keywords

  • Benefit transfer
  • Convergent validity
  • Function transfer
  • Meta-analysis

ASJC Scopus subject areas

  • Economics and Econometrics
  • Management, Monitoring, Policy and Law

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